}

  // Checks the expected result of shape inference for the given `op`.
  void CheckOutputShapes(
      TFE_Op* op,
      const std::vector<absl::optional<std::vector<int64_t>>>& input_shapes_vec,
      const std::vector<TF_Tensor*>& input_tensors,
      const absl::optional<std::vector<int64_t>>& expected_shape) {
    // Create input_shapes.
    TF_ShapeAndTypeList* input_shapes =

	// 31.11.15: Vector Integer Merge Instructions
	VMERGEVVM	V1, V2, V0, V3			// d781205c
	VMERGEVXM	X10, V2, V0, V3			// d741255c
	VMERGEVIM	$15, V2, V0, V3			// d7b1275c

	// 31.11.16: Vector Integer Move Instructions
	VMVVV		V2, V3				// d701015e
	VMVVX		X10, V3				// d741055e
	VMVVI		$15, V3				// d7b1075e

	// 31.12.1: Vector Single-Width Saturating Add and Subtract

        .createTestSuite();
  }

  // We are testing Vector / testing our tests on Vector.
  @SuppressWarnings("JdkObsolete")
  private Test testsForVector() {
    return ListTestSuiteBuilder.using(
            new TestStringListGenerator() {
              @Override
              protected List<String> create(String[] elements) {
                return new Vector<>(MinimalCollection.of(elements));
              }
            })

        .createTestSuite();
  }

  // We are testing Vector / testing our tests on Vector.
  @SuppressWarnings("JdkObsolete")
  private Test testsForVector() {
    return ListTestSuiteBuilder.using(
            new TestStringListGenerator() {
              @Override
              protected List<String> create(String[] elements) {
                return new Vector<>(MinimalCollection.of(elements));
              }
            })

  bool ret = TF_GetCode(s) == TF_OK;
  if (ret) ret = attr_value->ParseFromArray(buffer->data, buffer->length);
  TF_DeleteBuffer(buffer);
  return ret;
}

std::vector<std::pair<string, string>> GetGradDefs(
    const tensorflow::GraphDef& graph_def) {
  std::vector<std::pair<string, string>> grads;
  for (const tensorflow::GradientDef& grad : graph_def.library().gradient()) {
    grads.emplace_back(grad.function_name(), grad.gradient_func());

- *If total drives has many common divisors the algorithm chooses the minimum amounts of erasure sets possible for a erasure set size of any N*.  In the example with 1024 drives - 4, 8, 16 are GCD factors. With 16 drives we get a total of 64 possible sets, with 8 drives we get a total of 128 possible sets, with 4 drives we get a total of 256 possible sets. So algorithm automatically chooses 64 sets, which is *16* 64 = 1024* drives in total.

            if (mechs != null) {
                final ASN1EncodableVector vector = new ASN1EncodableVector();
                for (final ASN1ObjectIdentifier mech : mechs) {
                    vector.add(mech);
                }
                fields.add(new DERTaggedObject(true, 0, new DERSequence(vector)));
            }
            final int ctxFlags = getContextFlags();
            if (ctxFlags != 0) {

	MOVL (AX)(X0*1), AX             // ERROR "invalid instruction"
	MOVL (AX)(Y0*1), AX             // ERROR "invalid instruction"
	// VSIB/VM is invalid without vector index.
	// TODO(quasilyte): improve error message (#21860).
	// "invalid VSIB address (missing vector index)"
	VPGATHERQQ Y2, (BP), Y1         // ERROR "invalid instruction"
	// AVX2GATHER mask/index/dest #UD cases.

//     system.xml. Vendors can work with Veeam Product Management and the Alliances team on getting approval to integrate
//     specific system recommendations based on current support case statistics and storage performance possibilities.
//     Vendors might change the settings based on the configuration and scale out of the solution (more storage nodes =>

        }
        return out;
    }

    @Test
    @DisplayName("getNTHash: known vector for 'password'")
    void testGetNTHash_knownVector() {
        // Arrange
        String password = "password";
        // Known NT hash for "password" (UTF-16LE MD4)
        // This is a well-known test vector: password -> 8846F7EAEE8FB117AD06BDD830B7586C
        byte[] expected = hex("8846F7EAEE8FB117AD06BDD830B7586C");